Communities of knowledge and knowledge of communities: An appreciative inquiry into rural wellbeing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article offers a retrospective examination of the use of appreciative inquiry (AI) in a study on rural wellbeing. It provides a reflection on the rationale for choosing AI as a suitable methodology, critiques the application of AI in rural settings and considers its suitability for this inquiry into individual and community wellbeing. The article also considers the value of AI as a participatory research approach for community-university partnerships. A review of the literature on AI is distilled to examine the limitations as well as the utility of AI. Through an effective use of AI, communities of knowledge can be fostered and the knowledge of communities can be valued and harvested to enhance the wellbeing of rural communities.Keywords: appreciative inquiry, wellbeing, rural community, community-university partnerships
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it